Re: st: A layman question on model building

[John Antonakis <John.Antonakis@unil.ch>]

Hi:

If you have a sufficiently large sample size and the regressors of 
interest are significant predictors, then it is best to leave in the 
controls; they do not harm but help consistency (even if only a tad). 
What suffers is efficiency (standard errors) and this is amplified in 
small sample size conditions. I would (mostly) always err on the side of 
caution and include the controls. A couple of things to do before 
considering dropping are: (a) to do a Wald test to test whether the 
controls are simultaneously different from zero; (b) to do a Hausman 
test comparing the consistent and efficient estimator or a Chow test for 
the common regressors. See the first series of discussions (eq. 2-5c to 
see that consistency is never harmed even if a omitted regressor is not 
a significant predictor: Antonakis, J., Bendahan, S., Jacquart, P., & 
Lalive, R. 2010. On making causal claims: A review and recommendations. 
The Leadership Quarterly, 21(6): 1086-1120. 
http://www.hec.unil.ch/jantonakis/Causal_Claims.pdf

Best,
J.

__________________________________________

John Antonakis
Professor of Organizational Behavior
Director, Ph.D. Program in Management

Faculty of Business and Economics
University of Lausanne
Internef #618
CH-1015 Lausanne-Dorigny
Switzerland
Tel ++41 (0)21 692-3438
Fax ++41 (0)21 692-3305
http://www.hec.unil.ch/people/jantonakis

Associate Editor
The Leadership Quarterly
__________________________________________

On 07.03.2013 08:07, James Bernard wrote:
> Hi all,
>
> I have a question which may sound too basic, but I wonder if anyone could help:
>
> We often add control variables that turn out to be insignificant. Does
> that mean that I can remove that variable form my model without being
> concerned with omitted variable bias?
>
> Thanks,
> James
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